System, Method, and Computer Program Product for Cleaning Noisy Data from Unlabeled Datasets Using Autoencoders

ABSTRACT

Methods, systems, and computer program products are provided for cleaning noisy data from unlabeled datasets using autoencoders. A method includes receiving training data including noisy samples and other samples. An autoencoder network is trained based on the training data to increase a first metric based on the noisy samples and to reduce a second metric based on the other samples. Unlabeled data including unlabeled samples is received. A plurality of third outputs is generated by the autoencoder network based on the plurality of unlabeled samples. For each respective unlabeled sample, a respective third metric is determined based on the respective unlabeled sample and a respective third output, and whether to label the respective unlabeled sample as noisy or clean is determined based on the respective third metric and a threshold. Each respective unlabeled sample determined to be labeled as noisy is cleaned.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is the United States national phase of InternationalApplication No. PCT/US2022/042433 filed Sep. 2, 2022, and claims thebenefit of U.S. Provisional Patent Application No. 63/287,225, filed onDec. 8, 2021, the disclosures of which are hereby incorporated byreference in their entirety.

BACKGROUND 1. Technical Field

This disclosure relates generally to cleaning noisy data from unlabeleddatasets and, in non-limiting embodiments or aspects, systems, methods,and computer program products for cleaning noisy data from unlabeleddatasets using autoencoders.

2. Technical Considerations

Certain institutions have large amounts of data that may be used formachine learning tasks. For example, popular websites may have a largeamount of data describing using behavior, application developers mayhave large amounts of data input from users, or financial institutionsmay have large amounts of data related to transactions. Such data may beinput into machine learning models to train the models and/or to performtasks (e.g., classification, prediction, scoring, etc.) based on theinput.

However, if the data or a portion thereof is noisy, the data qualitywill be poor. As a result, the machine learning models using such datawill perform poorly. For example, accuracy of such models may bedecreased, training times for such models may increase, and/or the like.Identifying and/or cleaning noisy data samples from the data may bedifficult. For example, the data may not be labeled and/or otherwiseidentified as noisy.

SUMMARY

Accordingly, provided are improved systems, methods, and computerprogram products for cleaning noisy data from unlabeled datasets usingautoencoders.

According to non-limiting embodiments or aspects, provided is acomputer-implemented method for cleaning noisy data from unlabeleddatasets using autoencoders. A method may include receiving trainingdata including a plurality of noisy samples labeled as noisy and aplurality of other samples not labeled as noisy. An autoencoder networkmay be trained based on the training data to increase a first metricbased on the plurality of noisy samples and a plurality of first outputsgenerated by the autoencoder network using the plurality of noisysamples and to reduce a second metric based on the plurality of othersamples and a plurality of second outputs generated by the autoencodernetwork using the plurality of other samples. Unlabeled data including aplurality of unlabeled samples may be received. A plurality of thirdoutputs may be generated by the autoencoder network based on theplurality of unlabeled samples. For each respective unlabeled sample ofthe plurality of unlabeled samples, a respective third metric may bedetermined based on the respective unlabeled sample and a respectivethird output of the plurality of third outputs. For each respectiveunlabeled sample of the plurality of unlabeled samples, whether to labelthe respective unlabeled sample as noisy or clean may be determinedbased on the respective third metric and a threshold. For eachrespective unlabeled sample determined to be labeled as noisy, therespective unlabeled sample may be cleaned.

In some non-limiting embodiments or aspects, the plurality of othersamples may include a plurality of clean samples labeled as clean.

In some non-limiting embodiments or aspects, the plurality of othersamples may include a subset of the plurality of unlabeled samples.

In some non-limiting embodiments or aspects, the plurality of othersamples may include a second plurality of unlabeled samples. The methodmay further include labeling the second plurality of unlabeled samplesas clean.

In some non-limiting embodiments or aspects, the autoencoder network mayinclude a min-max adversarial hybrid autoencoder.

In some non-limiting embodiments or aspects, training the autoencodernetwork may include training the autoencoder network to maximize adifference between the plurality of noisy samples and the plurality offirst outputs and to minimize a difference between the plurality ofother samples and the plurality of second outputs.

In some non-limiting embodiments or aspects, training the autoencodernetwork may include determining a negative mean squared error based onthe plurality of noisy samples and the plurality of first outputs as afirst component of loss and/or determining a mean squared error based onthe plurality of other samples and the plurality of second outputs as asecond component of loss.

In some non-limiting embodiments or aspects, the third metric mayinclude a difference between each respective unlabeled sample and therespective third output. Additionally or alternatively, determiningwhether to label the respective unlabeled sample as noisy or clean mayinclude determining to label the respective unlabeled sample as noisy ifthe difference exceeds the threshold or determining to label therespective unlabeled sample as clean if the difference does not exceedthe threshold.

In some non-limiting embodiments or aspects, cleaning the respectiveunlabeled sample may include at least one of discarding the respectiveunlabeled sample, setting a respective flag indicating that therespective unlabeled sample is determined to be labeled as noisy,labeling the respective unlabeled sample as noisy, communicating a scorebased on the metric for the respective unlabeled sample, communicatingreport data based on determining whether to label each respectiveunlabeled sample as noisy or clean, and/or any combination thereof.

In some non-limiting embodiments or aspects, the plurality of noisysamples may include a plurality of declined transactions, the pluralityof other samples may include a first plurality of approved transactions,and the plurality of unlabeled samples may include a second plurality ofapproved transactions. In some non-limiting embodiments or aspects,determining whether to label each respective unlabeled sample as noisyor clean may include determining whether to label each respectiveunlabeled sample as declined or approved, respectively. Additionally oralternatively, cleaning each respective unlabeled sample determined tobe labeled as noisy may include discarding the respective unlabeledsample. In some non-limiting embodiments or aspects, a remainingplurality of unlabeled samples includes each respective unlabeled sampledetermined to be labeled as clean.

In some non-limiting embodiments or aspects, the method may furtherinclude retraining the autoencoder network to increase a further firstmetric based on the plurality of declined transactions and a furtherplurality of first outputs generated by the autoencoder network usingthe plurality of declined transactions and to reduce a further secondmetric based on the remaining plurality of unlabeled samples and afurther plurality of second outputs generated by the autoencoder networkusing the remaining plurality of unlabeled samples.

In some non-limiting embodiments or aspects, receiving the training datamay include receiving the training data from a user device. Additionallyor alternatively, receiving the unlabeled data may include receiving theunlabeled data from the user device.

In some non-limiting embodiments or aspects, cleaning may includegenerating report data based on determining whether to label eachrespective unlabeled sample as noisy or clean and/or communicating thereport data to the user device.

According to non-limiting embodiments or aspects, provided is a systemfor cleaning noisy data from unlabeled datasets using autoencoders. Asystem may include a data cleaning system configured to receive trainingdata including a plurality of noisy samples labeled as noisy and aplurality of other samples not labeled as noisy; train an autoencodernetwork based on the training data to increase a first metric based onthe plurality of noisy samples and a plurality of first outputsgenerated by the autoencoder network using the plurality of noisysamples and to reduce a second metric based on the plurality of othersamples and a plurality of second outputs generated by the autoencodernetwork using the plurality of other samples; receive unlabeled dataincluding a plurality of unlabeled samples; generate a plurality ofthird outputs by the autoencoder network based on the plurality ofunlabeled samples; for each respective unlabeled sample of the pluralityof unlabeled samples, determine a respective third metric based on therespective unlabeled sample and a respective third output of theplurality of third outputs; for each respective unlabeled sample of theplurality of unlabeled samples, determine whether to label therespective unlabeled sample as noisy or clean based on the respectivethird metric and a threshold; and for each respective unlabeled sampledetermined to be labeled as noisy, clean the respective unlabeledsample.

In some non-limiting embodiments or aspects, the system may furtherinclude an input data database configured to receive the training datafrom a user device, receive the unlabeled data from the user device, andcommunicate the training data and the unlabeled data to the datacleaning system.

In some non-limiting embodiments or aspects, cleaning may includegenerating report data based on determining whether to label eachrespective unlabeled sample as noisy or clean and communicating thereport data.

In some non-limiting embodiments or aspects, the system may furtherinclude an output data database configured to receive the report datafrom the data cleaning system and communicate the report data to a userdevice.

In some non-limiting embodiments or aspects, the data cleaning systemmay be part of a transaction service provider system, and a user devicemay be part of an issuer system.

According to non-limiting embodiments or aspects, provided is a computerprogram product for cleaning noisy data from unlabeled datasets usingautoencoders. A computer program product may include at least onenon-transitory computer-readable medium including one or moreinstructions that, when executed by at least one processor, cause the atleast one processor to: receive training data including a plurality ofnoisy samples labeled as noisy and a plurality of other samples notlabeled as noisy; train an autoencoder network based on the trainingdata to increase a first metric based on the plurality of noisy samplesand a plurality of first outputs generated by the autoencoder networkusing the plurality of noisy samples and to reduce a second metric basedon the plurality of other samples and a plurality of second outputsgenerated by the autoencoder network using the plurality of othersamples; receive unlabeled data including a plurality of unlabeledsamples; generate a plurality of third outputs by the autoencodernetwork based on the plurality of unlabeled samples; for each respectiveunlabeled sample of the plurality of unlabeled samples, determine arespective third metric based on the respective unlabeled sample and arespective third output of the plurality of third outputs; for eachrespective unlabeled sample of the plurality of unlabeled samples,determine whether to label the respective unlabeled sample as noisy orclean based on the respective third metric and a threshold; and for eachrespective unlabeled sample determined to be labeled as noisy, clean therespective unlabeled sample.

In some non-limiting embodiments or aspects, the plurality of noisysamples may include a plurality of declined transactions, the pluralityof other samples may include a first plurality of approved transactions,and the plurality of unlabeled samples may include a second plurality ofapproved transactions.

Further non-limiting embodiments or aspects will be set forth in thefollowing numbered clauses:

-   -   Clause 1: A computer-implemented method, comprising: receiving,        with at least one processor, training data comprising a        plurality of noisy samples labeled as noisy and a plurality of        other samples not labeled as noisy; training, with at least one        processor, an autoencoder network based on the training data to        increase a first metric based on the plurality of noisy samples        and a plurality of first outputs generated by the autoencoder        network using the plurality of noisy samples and to reduce a        second metric based on the plurality of other samples and a        plurality of second outputs generated by the autoencoder network        using the plurality of other samples; receiving, with at least        one processor, unlabeled data comprising a plurality of        unlabeled samples; generating, with at least one processor, a        plurality of third outputs by the autoencoder network based on        the plurality of unlabeled samples; for each respective        unlabeled sample of the plurality of unlabeled samples,        determining, with at least one processor, a respective third        metric based on the respective unlabeled sample and a respective        third output of the plurality of third outputs; for each        respective unlabeled sample of the plurality of unlabeled        samples, determining, with at least one processor, whether to        label the respective unlabeled sample as noisy or clean based on        the respective third metric and a threshold; and for each        respective unlabeled sample determined to be labeled as noisy,        cleaning, with at least one processor, the respective unlabeled        sample.    -   Clause 2: The method of clause 1, wherein the plurality of other        samples comprises a plurality of clean samples labeled as clean.    -   Clause 3: The method of clause 1 or 2, wherein the plurality of        other samples comprises a subset of the plurality of unlabeled        samples.    -   Clause 4: The method of any of clauses 1-3, wherein the        plurality of other samples comprises a second plurality of        unlabeled samples, the method further comprising: labeling, with        at least one processor, the second plurality of unlabeled        samples as clean.    -   Clause 5: The method of any of clauses 1-4, wherein the        autoencoder network comprises a min-max adversarial hybrid        autoencoder.    -   Clause 6: The method of any of clauses 1-5, wherein training the        autoencoder network comprises training the autoencoder network        to maximize a difference between the plurality of noisy samples        and the plurality of first outputs and to minimize a difference        between the plurality of other samples and the plurality of        second outputs.    -   Clause 7: The method of any of clauses 1-6, wherein training the        autoencoder network comprises: determining a negative mean        squared error based on the plurality of noisy samples and the        plurality of first outputs as a first component of loss; and        determining a mean squared error based on the plurality of other        samples and the plurality of second outputs as a second        component of loss.    -   Clause 8: The method of any of clauses 1-7, wherein the third        metric comprises a difference between each respective unlabeled        sample and the respective third output, and wherein determining        whether to label the respective unlabeled sample as noisy or        clean comprises: determining to label the respective unlabeled        sample as noisy if the difference exceeds the threshold; or        determining to label the respective unlabeled sample as clean if        the difference does not exceed the threshold.    -   Clause 9: The method of any of clauses 1-8, wherein cleaning the        respective unlabeled sample comprises at least one of:        discarding the respective unlabeled sample; setting a respective        flag indicating that the respective unlabeled sample is        determined to be labeled as noisy; labeling the respective        unlabeled sample as noisy; communicating a score based on the        metric for the respective unlabeled sample; communicating report        data based on determining whether to label each respective        unlabeled sample as noisy or clean; or any combination thereof.    -   Clause 10: The method of any of clauses 1-9, wherein the        plurality of noisy samples comprises a plurality of declined        transactions, the plurality of other samples comprises a first        plurality of approved transactions, and the plurality of        unlabeled samples comprises a second plurality of approved        transactions, wherein determining whether to label each        respective unlabeled sample as noisy or clean comprises        determining whether to label each respective unlabeled sample as        declined or approved, respectively, wherein cleaning each        respective unlabeled sample determined to be labeled as noisy        comprises discarding the respective unlabeled sample, and        wherein a remaining plurality of unlabeled samples comprises        each respective unlabeled sample determined to be labeled as        clean.    -   Clause 11: The method of any of clauses 1-10, further        comprising: retraining, with at least one processor, the        autoencoder network to increase a further first metric based on        the plurality of declined transactions and a further plurality        of first outputs generated by the autoencoder network using the        plurality of declined transactions and to reduce a further        second metric based on the remaining plurality of unlabeled        samples and a further plurality of second outputs generated by        the autoencoder network using the remaining plurality of        unlabeled samples.    -   Clause 12: The method of any of clauses 1-11, wherein receiving        the training data comprises receiving the training data from a        user device; and wherein receiving the unlabeled data comprises        receiving the unlabeled data from the user device.    -   Clause 13: The method of any of clauses 1-12, wherein cleaning        comprises: generating report data based on determining whether        to label each respective unlabeled sample as noisy or clean; and        communicating the report data to the user device.    -   Clause 14: A system, comprising: a data cleaning system        configured to: receive training data comprising a plurality of        noisy samples labeled as noisy and a plurality of other samples        not labeled as noisy; train an autoencoder network based on the        training data to increase a first metric based on the plurality        of noisy samples and a plurality of first outputs generated by        the autoencoder network using the plurality of noisy samples and        to reduce a second metric based on the plurality of other        samples and a plurality of second outputs generated by the        autoencoder network using the plurality of other samples;        receive unlabeled data comprising a plurality of unlabeled        samples; generate a plurality of third outputs by the        autoencoder network based on the plurality of unlabeled samples;        for each respective unlabeled sample of the plurality of        unlabeled samples, determine a respective third metric based on        the respective unlabeled sample and a respective third output of        the plurality of third outputs; for each respective unlabeled        sample of the plurality of unlabeled samples, determine whether        to label the respective unlabeled sample as noisy or clean based        on the respective third metric and a threshold; and for each        respective unlabeled sample determined to be labeled as noisy,        clean the respective unlabeled sample.    -   Clause 15: The system of clause 14, further comprising: an input        data database configured to: receive the training data from a        user device receive the unlabeled data from the user device; and        communicate the training data and the unlabeled data to the data        cleaning system.    -   Clause 16: The system of clause 14 or 15, wherein cleaning        comprises generating report data based on determining whether to        label each respective unlabeled sample as noisy or clean and        communicating the report data.    -   Clause 17: The system of any of clauses 14-16, further        comprising: an output data database configured to: receive the        report data from the data cleaning system; and communicate the        report data to a user device.    -   Clause 18: The system of any of clauses 14-17, wherein the data        cleaning system comprises part of a transaction service provider        system, and wherein a user device comprises part of an issuer        system.    -   Clause 19: A computer program product comprising at least one        non-transitory computer-readable medium including one or more        instructions that, when executed by at least one processor,        cause the at least one processor to: receive training data        comprising a plurality of noisy samples labeled as noisy and a        plurality of other samples not labeled as noisy; train an        autoencoder network based on the training data to increase a        first metric based on the plurality of noisy samples and a        plurality of first outputs generated by the autoencoder network        using the plurality of noisy samples and to reduce a second        metric based on the plurality of other samples and a plurality        of second outputs generated by the autoencoder network using the        plurality of other samples; receive unlabeled data comprising a        plurality of unlabeled samples; generate a plurality of third        outputs by the autoencoder network based on the plurality of        unlabeled samples; for each respective unlabeled sample of the        plurality of unlabeled samples, determine a respective third        metric based on the respective unlabeled sample and a respective        third output of the plurality of third outputs; for each        respective unlabeled sample of the plurality of unlabeled        samples, determine whether to label the respective unlabeled        sample as noisy or clean based on the respective third metric        and a threshold; and for each respective unlabeled sample        determined to be labeled as noisy, clean the respective        unlabeled sample.    -   Clause 20: The computer program product of clause 19, wherein        the plurality of noisy samples comprises a plurality of declined        transactions, the plurality of other samples comprises a first        plurality of approved transactions, and the plurality of        unlabeled samples comprises a second plurality of approved        transactions.

These and other features and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structures and the combination of parts and economies ofmanufacture, will become more apparent upon consideration of thefollowing description and the appended claims with reference to theaccompanying drawings, all of which form a part of this specification,wherein like reference numerals designate corresponding parts in thevarious figures. It is to be expressly understood, however, that thedrawings are for the purpose of illustration and description only andare not intended as a definition of the limits of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

Additional advantages and details are explained in greater detail belowwith reference to the non-limiting, exemplary embodiments that areillustrated in the accompanying schematic figures, in which:

FIG. 1 is a schematic diagram of a system for cleaning noisy data fromunlabeled datasets using autoencoders according to some non-limitingembodiments or aspects;

FIG. 2 is a flow diagram for a method for cleaning noisy data fromunlabeled datasets using autoencoders according to some non-limitingembodiments or aspects;

FIG. 3A is a diagram for an exemplary implementation of the systems andmethods described herein according to some non-limiting embodiments oraspects;

FIG. 3B is a graph for an exemplary metric for the implementation ofFIG. 3A according to some non-limiting embodiments or aspects;

FIG. 4 is a diagram of a non-limiting embodiment or aspect of anenvironment in which methods, systems, and/or computer program products,described herein, may be implemented according to some non-limitingembodiments or aspects; and

FIG. 5 illustrates example components of a device used in connectionwith non-limiting embodiments or aspects.

DETAILED DESCRIPTION

For purposes of the description hereinafter, the terms “end,” “upper,”“lower,” “right,” “left,” “vertical,” “horizontal,” “top,” “bottom,”“lateral,” “longitudinal,” and derivatives thereof shall relate to theembodiments as they are oriented in the drawing figures. However, it isto be understood that the embodiments may assume various alternativevariations and step sequences, except where expressly specified to thecontrary. It is also to be understood that the specific devices andprocesses illustrated in the attached drawings, and described in thefollowing specification, are simply exemplary embodiments or aspects ofthe disclosure. Hence, specific dimensions and other physicalcharacteristics related to the embodiments or aspects disclosed hereinare not to be considered as limiting.

No aspect, component, element, structure, act, step, function,instruction, and/or the like used herein should be construed as criticalor essential unless explicitly described as such. Also, as used herein,the articles “a” and “an” are intended to include one or more items andmay be used interchangeably with “one or more” and “at least one.”Furthermore, as used herein, the term “set” is intended to include oneor more items (e.g., related items, unrelated items, a combination ofrelated and unrelated items, and/or the like) and may be usedinterchangeably with “one or more” or “at least one.” Where only oneitem is intended, the term “one” or similar language is used. Also, asused herein, the terms “has,” “have,” “having,” or the like are intendedto be open-ended terms. Further, the phrase “based on” is intended tomean “based at least partially on” unless explicitly stated otherwise.

As used herein, the term “acquirer institution” may refer to an entitylicensed and/or approved by a transaction service provider to originatetransactions (e.g., payment transactions) using a payment deviceassociated with the transaction service provider. The transactions theacquirer institution may originate may include payment transactions(e.g., purchases, original credit transactions (OCTs), account fundingtransactions (AFTs), and/or the like). In some non-limiting embodimentsor aspects, an acquirer institution may be a financial institution, suchas a bank. As used herein, the term “acquirer system” may refer to oneor more computing devices operated by or on behalf of an acquirerinstitution, such as a server computer executing one or more softwareapplications.

As used herein, the term “account identifier” may include one or moreprimary account numbers (PANs), tokens, or other identifiers associatedwith a customer account. The term “token” may refer to an identifierthat is used as a substitute or replacement identifier for an originalaccount identifier, such as a PAN. Account identifiers may bealphanumeric or any combination of characters and/or symbols. Tokens maybe associated with a PAN or other original account identifier in one ormore data structures (e.g., one or more databases, and/or the like) suchthat they may be used to conduct a transaction without directly usingthe original account identifier. In some examples, an original accountidentifier, such as a PAN, may be associated with a plurality of tokensfor different individuals or purposes.

As used herein, the term “communication” may refer to the reception,receipt, transmission, transfer, provision, and/or the like of data(e.g., information, signals, messages, instructions, commands, and/orthe like). For one unit (e.g., a device, a system, a component of adevice or system, combinations thereof, and/or the like) to be incommunication with another unit means that the one unit is able todirectly or indirectly receive information from and/or transmitinformation to the other unit. This may refer to a direct or indirectconnection (e.g., a direct communication connection, an indirectcommunication connection, and/or the like) that is wired and/or wirelessin nature. Additionally, two units may be in communication with eachother even though the information transmitted may be modified,processed, relayed, and/or routed between the first and second unit. Forexample, a first unit may be in communication with a second unit eventhough the first unit passively receives information and does notactively transmit information to the second unit. As another example, afirst unit may be in communication with a second unit if at least oneintermediary unit processes information received from the first unit andcommunicates the processed information to the second unit.

As used herein, the term “computing device” may refer to one or moreelectronic devices configured to process data. A computing device may,in some examples, include the necessary components to receive, process,and output data, such as a processor, a display, a memory, an inputdevice, a network interface, and/or the like. A computing device may bea mobile device. As an example, a mobile device may include a cellularphone (e.g., a smartphone or standard cellular phone), a portablecomputer, a wearable device (e.g., watches, glasses, lenses, clothing,and/or the like), a personal digital assistant (PDA), and/or other likedevices. A computing device may also be a desktop computer or other formof non-mobile computer.

As used herein, the terms “electronic wallet” and “electronic walletapplication” refer to one or more electronic devices and/or softwareapplications configured to initiate and/or conduct payment transactions.For example, an electronic wallet may include a mobile device executingan electronic wallet application, and may further include server-sidesoftware and/or databases for maintaining and providing transaction datato the mobile device. An “electronic wallet provider” may include anentity that provides and/or maintains an electronic wallet for acustomer, such as Google Pay®, Android Pay®, Apple Pay®, Samsung Pay®,and/or other like electronic payment systems. In some non-limitingexamples, an issuer bank may be an electronic wallet provider.

As used herein, the term “issuer institution” may refer to one or moreentities, such as a bank, that provide accounts to customers forconducting transactions (e.g., payment transactions), such as initiatingcredit and/or debit payments. For example, an issuer institution mayprovide an account identifier, such as a PAN, to a customer thatuniquely identifies one or more accounts associated with that customer.The account identifier may be embodied on a portable financial device,such as a physical financial instrument, e.g., a payment card, and/ormay be electronic and used for electronic payments. The term “issuersystem” refers to one or more computer devices operated by or on behalfof an issuer institution, such as a server computer executing one ormore software applications. For example, an issuer system may includeone or more authorization servers for authorizing a transaction.

As used herein, the term “merchant” may refer to an individual or entitythat provides goods and/or services, or access to goods and/or services,to customers based on a transaction, such as a payment transaction. Theterm “merchant” or “merchant system” may also refer to one or morecomputer systems operated by or on behalf of a merchant, such as aserver computer executing one or more software applications. A“point-of-sale (POS) system,” as used herein, may refer to one or morecomputers and/or peripheral devices used by a merchant to engage inpayment transactions with customers, including one or more card readers,near-field communication (NFC) receivers, radio frequency identification(RFID) receivers, and/or other contactless transceivers or receivers,contact-based receivers, payment terminals, computers, servers, inputdevices, and/or other like devices that can be used to initiate apayment transaction.

As used herein, the term “payment device” may refer to a portablefinancial device, an electronic payment device, a payment card (e.g., acredit or debit card), a gift card, a smartcard, smart media, a payrollcard, a healthcare card, a wristband, a machine-readable mediumcontaining account information, a keychain device or fob, an RFIDtransponder, a retailer discount or loyalty card, a cellular phone, anelectronic wallet mobile application, a PDA, a pager, a security card, acomputing device, an access card, a wireless terminal, a transponder,and/or the like. In some non-limiting embodiments or aspects, thepayment device may include volatile or non-volatile memory to storeinformation (e.g., an account identifier, a name of the account holder,and/or the like).

As used herein, the term “payment gateway” may refer to an entity and/ora payment processing system operated by or on behalf of such an entity(e.g., a merchant service provider, a payment service provider, apayment facilitator, a payment facilitator that contracts with anacquirer, a payment aggregator, and/or the like), which provides paymentservices (e.g., transaction service provider payment services, paymentprocessing services, and/or the like) to one or more merchants. Thepayment services may be associated with the use of portable financialdevices managed by a transaction service provider. As used herein, theterm “payment gateway system” may refer to one or more computer systems,computer devices, servers, groups of servers, and/or the like, operatedby or on behalf of a payment gateway.

As used herein, the term “server” may refer to or include one or morecomputing devices that are operated by or facilitate communication andprocessing for multiple parties in a network environment, such as theInternet, although it will be appreciated that communication may befacilitated over one or more public or private network environments andthat various other arrangements are possible. Further, multiplecomputing devices (e.g., servers, point-of-sale (POS) devices, mobiledevices, etc.) directly or indirectly communicating in the networkenvironment may constitute a “system.” Reference to “a server” or “aprocessor,” as used herein, may refer to a previously-recited serverand/or processor that is recited as performing a previous step orfunction, a different server and/or processor, and/or a combination ofservers and/or processors. For example, as used in the specification andthe claims, a first server and/or a first processor that is recited asperforming a first step or function may refer to the same or differentserver and/or a processor recited as performing a second step orfunction.

As used herein, the term “transaction service provider” may refer to anentity that receives transaction authorization requests from merchantsor other entities and provides guarantees of payment, in some casesthrough an agreement between the transaction service provider and anissuer institution. For example, a transaction service provider mayinclude a payment network such as Visa® or any other entity thatprocesses transactions. The term “transaction processing system” mayrefer to one or more computer systems operated by or on behalf of atransaction service provider, such as a transaction processing serverexecuting one or more software applications. A transaction processingserver may include one or more processors and, in some non-limitingembodiments or aspects, may be operated by or on behalf of a transactionservice provider.

Non-limiting embodiments or aspects of the disclosed subject matter aredirected to systems, methods, and computer program products for cleaningnoisy data from unlabeled datasets using autoencoders. For example,non-limiting embodiments or aspects of the disclosed subject matterprovide using training data including some noisy samples labeled asnoisy and other samples not labeled as noisy to train an autoencodernetwork (e.g., a min-max adversarial hybrid autoencoder) to increase afirst metric based on (e.g., difference between) the noisy samples andfirst outputs generated based thereon and to reduce a second metricbased on (e.g., difference between) the other samples and second outputsgenerated based thereon so that, when unlabeled data is received, thetrained autoencoder network may generate third outputs based thereof anddetermine whether each unlabeled sample is noisy based on a third metric(e.g., difference between each respective unlabeled sample and therespective third output based thereof). Such embodiments or aspectsprovide techniques and systems that enable identification of unlabeledsamples as noisy (or clean) and/or that enable cleaning of noisysamples. Accordingly, data quality is improved due to the identificationand/or cleaning of noisy samples from the data. Additionally,performance of downstream machine learning models is improved (e.g.,accuracy may be increased, training times may be decreased, and/or thelike) by using the cleaned data. Moreover, the min-max adversarialhybrid autoencoder enables functionality that is not possible withtraditional autoencoders. For example, the min-max adversarial hybridautoencoder may enable labeling data as noisy (or clean), assessing dataquality, and/or cleaning data.

For the purpose of illustration, in the following description, while thepresently disclosed subject matter is described with respect to systems,methods, and computer program products for cleaning noisy data fromunlabeled datasets using autoencoders, e.g., for transaction data andhandwriting sample data, one skilled in the art will recognize that thedisclosed subject matter is not limited to the illustrative embodiments.For example, the systems, methods, and computer program productsdescribed herein may be used with a wide variety of settings, such ascleaning noisy data from unlabeled datasets using autoencoders for anysuitable type of data, e.g., data describing using behavior on websites,data input from users into applications, and/or the like.

FIG. 1 depicts a system 100 for cleaning noisy data from unlabeleddatasets using autoencoders according to some non-limiting embodimentsor aspects. As shown in FIG. 1 , system 100 includes data cleaningsystem 102, user device 104, input data database 106, and output datadatabase 108.

Data cleaning system 102 may include one or more devices capable ofreceiving information from and/or communicating information to userdevice 104, input data database 106, and/or output data database 108.For example, data cleaning system 102 may include a computing device,such as a server, a group of servers, and/or other like devices. In somenon-limiting embodiments or aspects, data cleaning system 102 may be incommunication with a data storage device, which may be local or remoteto data cleaning system 102. In some non-limiting embodiments oraspects, data cleaning system 102 may be capable of receivinginformation from, storing information in, communicating information to,or searching information stored in the data storage device. In somenon-limiting embodiments or aspects, data cleaning system 102 may beassociated with a transaction service provider, as described herein.

User device 104 may include one or more devices capable of receivinginformation from and/or communicating information to data cleaningsystem 102, input data database 106, and output data database 108. Forexample, user device 104 may include a computing device, such as amobile device, a portable computer, a desktop computer, and/or otherlike devices.

Input data database 106 may include one or more devices capable ofreceiving information from and/or communicating information to datacleaning system 102, user device 104, and/or output data database 108.For example, input data database 106 may include a computing device,such as a server, a group of servers, and/or other like devices. In somenon-limiting embodiments or aspects, input data database 106 may be incommunication with a data storage device, which may be local or remoteto input data database 106. In some non-limiting embodiments or aspects,input data database 106 may be capable of receiving information from,storing information in, communicating information to, or searchinginformation stored in the data storage device. In some non-limitingembodiments or aspects, input data database 106 may be associated with(e.g., a part of) data cleaning system 102.

Output data database 108 may include one or more devices capable ofreceiving information from and/or communicating information to datacleaning system 102, user device 104, and/or input data database 106.For example, output data database 108 may include a computing device,such as a server, a group of servers, and/or other like devices. In somenon-limiting embodiments or aspects, output data database 108 may be incommunication with a data storage device, which may be local or remoteto output data database 108. In some non-limiting embodiments oraspects, output data database 108 may be capable of receivinginformation from, storing information in, communicating information to,or searching information stored in the data storage device. In somenon-limiting embodiments or aspects, output data database 108 may beassociated with (e.g., a part of) data cleaning system 102. Additionallyor alternatively, in some non-limiting embodiments or aspects, inputdata database 106 and output data database 108 may be implemented withina single database.

In some non-limiting embodiments or aspects, data cleaning system 102may include autoencoder network 120. For example, autoencoder network120 may include encoder network 130, latent layer 140, and decodernetwork 150. In some non-limiting embodiments, encoder network 130 mayinclude input layer 132 and/or at least one hidden layer 134.Additionally or alternatively, decoder network 150 may include at leastone hidden layer 152 and output layer 154. In some non-limitingembodiments or aspects, data cleaning system 102 may receive (e.g., frominput data database 106 and/or user device 104) data, which may includenoisy samples 111 and/or clean samples 112. The data (e.g., noisysamples 111 and/or clean samples 112) may be input into autoencodernetwork 120 to generate outputs, which may include noisy reconstructedsamples 113 and/or clean reconstructed samples 114, respectively. Forexample, after autoencoder network 120 is trained, a metric based on(e.g., difference between) noisy samples 111 and noisy reconstructedsamples 113 may be increased (e.g., maximized) and/or a metric based on(e.g., difference between) clean samples 112 and clean reconstructedsamples 114 may be reduced (e.g., minimized), as described herein. Basedon the respective metrics, data cleaning system 102 may determinewhether each respective input sample (e.g., noisy sample 111 or cleansample 112) is noisy or clean, respectively, as described herein.Additionally or alternatively, data cleaning system 102 may clean (e.g.,discard, set a flag associated with, label, score, report, and/or thelike) each noisy sample 111, as described herein. In some non-limitingembodiments or aspects, data cleaning system 102 may communicate (e.g.,to output data database 108 and/or user device 104) the outputs (noisyreconstructed samples 113 and/or clean reconstructed samples 114),cleaned data (e.g., clean samples 112 after discarding noisy sample111), a report based on determining whether each input sample is noisyor clean, and/or any combination thereof.

The number and arrangement of systems and devices shown in FIG. 1 areprovided as an example. There may be additional systems and/or devices,fewer systems and/or devices, different systems and/or devices, and/ordifferently arranged systems and/or devices than those shown in FIG. 1 .Furthermore, two or more systems or devices shown in FIG. 1 may beimplemented within a single system or device, or a single system ordevice shown in FIG. 1 may be implemented as multiple, distributedsystems or devices. Additionally or alternatively, a set of systems(e.g., one or more systems) or a set of devices (e.g., one or moredevices) of system 100 may perform one or more functions described asbeing performed by another set of systems or another set of devices ofsystem 100.

Referring now to FIG. 2 , shown is a process 200 for cleaning noisy datafrom unlabeled datasets using autoencoders according to somenon-limiting embodiments or aspects. The steps shown in FIG. 2 are forexample purposes only. It will be appreciated that additional, fewer,different, and/or a different order of steps may be used in non-limitingembodiments or aspects. In some non-limiting embodiments or aspects, oneor more of the steps of process 200 may be performed (e.g., completely,partially, and/or the like) by data cleaning system 102 (e.g., one ormore devices of data cleaning system 102). In some non-limitingembodiments or aspects, one or more of the steps of process 200 may beperformed (e.g., completely, partially, and/or the like) by anothersystem, another device, another group of systems, or another group ofdevices, separate from or including data cleaning system 102, such asuser device 104, input data database 106, and output data database 108.

As shown in FIG. 2 , at step 202, process 200 may include receivingtraining data. For example, data cleaning system 102 may receivetraining data from input data database 106 and/or user device 104. Insome non-limiting embodiments or aspects, user device 104 maycommunicate the training data to input data database 106 (e.g., beforedata cleaning system 102 receives training data from input data database106). In some non-limiting embodiments or aspects, user device 104 maycommunicate the training data to data cleaning system 102. In somenon-limiting embodiments or aspects, input data database 106 maycommunicate the training data to data cleaning system 102.

In some non-limiting embodiments or aspects, the training data mayinclude a plurality of noisy samples 111 labeled as noisy and aplurality of other samples not labeled as noisy. For example, the othersamples may include a plurality of clean samples 112, which may belabeled as clean. Additionally or alternatively, the other samples mayinclude a plurality of unlabeled samples. In some non-limitingembodiments or aspects, data cleaning system 102 may label the (subsetof) unlabeled samples of the training data as clean (e.g., fortraining).

In some non-limiting embodiments or aspects, data cleaning system 102may receive (e.g., from input data database 106 and/or user device 104)input data including a plurality of noisy samples labeled as noisy and aplurality of unlabeled samples. Data cleaning system 102 may divide theinput data into training data and remaining data. For example, thetraining data may include the noisy samples (e.g., all of the sampleslabeled as noisy) and a subset of the unlabeled samples. Additionally oralternatively, data cleaning system 102 may save (e.g., store) theremaining unlabeled samples (e.g., as unlabeled data) for use aftertraining autoencoder network 120, as described herein. In somenon-limiting embodiments or aspects, data cleaning system 102 may labelthe (subset of) the unlabeled samples of the training data as clean(e.g., for training).

As shown in FIG. 2 , at step 204, process 200 may include training anautoencoder network. For example, data cleaning system 102 may trainautoencoder network 120 based on the training data. In some non-limitingembodiments or aspects, data cleaning system 102 may train autoencodernetwork 120 based on the training data to increase a first metric basedon noisy samples 111 and first outputs (e.g., noisy reconstructedsamples 113 generated by autoencoder network 120 using noisy samples 111as input) and to reduce a second metric based on the other samples(e.g., clean samples 112) and second outputs (e.g., clean reconstructedsamples 114 generated by autoencoder network 120 using the other samplesas input). Alternatively, data cleaning system 102 may train autoencodernetwork 120 to decrease the first metric and increase the second metric,depending on the type of metrics being used.

In some non-limiting embodiments or aspects, each metric (e.g., firstmetric and second metric) may be the difference between the respectiveinput (e.g., noisy sample 111 or clean sample 112) and the respectiveoutput (e.g., noisy reconstructed sample 113 or clean reconstructedsample 114, respectively). In some non-limiting embodiments or aspects,each metric may be score (e.g., similarity score and/or the like) basedon the respective input and the respective output.

In some non-limiting embodiments or aspects, autoencoder network 120 mayinclude a new type of autoencoder network herein referred to as amin-max adversarial hybrid autoencoder. A min-max adversarial hybridautoencoder may include an autoencoder network with a hybrid (e.g.,dual) objective function to increase (e.g., maximize) a first metricbased on (e.g., difference between) noisy samples 111 and first outputsbased thereon (e.g., noisy reconstructed samples 113) and reduce (e.g.,minimize) a second metric based on (e.g., difference between) the othersamples (e.g., clean samples 112) and second outputs based thereof(e.g., clean reconstructed samples 114). For example, the objectivefunction for the min-max adversarial hybrid autoencoder may berepresented by the following equation:

F*=argmax_(F) ∥X _(n) −F(X _(n))∥_(D)+argmin_(F) ∥X _(c) −F(X_(c))∥_(D),

wherein D is the metric, X_(n) is the plurality of noisy samples, X_(c)is the plurality of other samples, F(X_(n)) is the output of autoencodernetwork 120 for the noisy samples, F(X_(c)) is the output of autoencodernetwork 120 for the other/clean samples, F is a shorthand notation forthe function that transforms the inputs of autoencoder network 120 tothe outputs, and F* is F with parameters to satisfy the objectionfunction. Thus, in some non-limiting embodiments or aspects, datacleaning system 102 may train autoencoder network 120 (e.g., the min-maxadversarial hybrid autoencoder) to maximize a difference between thenoisy samples 111 and noisy reconstructed samples 113 and to minimize adifference between clean samples 112 and clean reconstructed samples114.

In some non-limiting embodiments or aspects, the loss for autoencodernetwork 120 (e.g., the min-max adversarial hybrid autoencoder) may bedetermined differently based on whether the input is noisy (e.g., noisysamples 111) or unlabeled/clean (e.g., clean samples 112). For example,during training of autoencoder network 120, data cleaning system 102 maydetermine a negative mean squared error based on noisy samples 111 andoutputs generated based thereon (e.g., noisy reconstructed samples 113)as loss (e.g., a first component of loss). Additionally oralternatively, data cleaning system 102 may determine a (positive) meansquared error based on the other samples (e.g., clean samples 112) andoutputs generated based thereon (e.g., clean reconstructed samples 114)as loss (e.g., a second component of loss). Accordingly, in somenon-limiting embodiments or aspects, data cleaning system 102 may trainautoencoder network 120 based on the training data, the above objectivefunction, and the above determinations of loss.

In some non-limiting embodiments or aspects, training may includeinputting (e.g., by data cleaning system 102) each respective inputsample (e.g., each noisy sample 111 and/or each other sample/cleansample 112) into autoencoder network 120 to generate a respective output(e.g., a respective noisy reconstructed sample 113 or a respective cleanreconstructed sample 114). A respective (component of) loss may bedetermined based on each respective input sample and each respectiveoutput. The loss (or each component thereof) may be back-propagatedthrough autoencoder network 120 to update the parameters (e.g., weights)thereof. This training process may be repeated until a terminationcondition is satisfied. For example, a termination condition may includea target number of epochs, a target loss value, a target accuracy,and/or the like.

As shown in FIG. 2 , at step 206, process 200 may include receivingunlabeled data. For example, data cleaning system 102 may receiveunlabeled data from input data database 106 and/or user device 104. Insome non-limiting embodiments or aspects, user device 104 maycommunicate the unlabeled data to input data database 106 (e.g., beforedata cleaning system 102 receives unlabeled data from input datadatabase 106). In some non-limiting embodiments or aspects, user device104 may communicate the unlabeled data to data cleaning system 102. Insome non-limiting embodiments or aspects, input data database 106 maycommunicate the unlabeled data to data cleaning system 102. In somenon-limiting embodiments or aspects, the unlabeled data may include aplurality of unlabeled samples.

In some non-limiting embodiments or aspects, as described above, datacleaning system 102 may receive (e.g., from input data database 106and/or user device 104) input data including a plurality of noisysamples labeled as noisy and a plurality of unlabeled samples. Datacleaning system 102 may divide the input data into training data andremaining data, as described herein. For example, the remainingunlabeled samples (e.g., after data cleaning system separates thetraining data from the input data) may be used as the unlabeled data.

As shown in FIG. 2 , at step 208, process 200 may include generatingoutputs by the autoencoder network. For example, data cleaning system102 may input each respective unlabeled sample into autoencoder network120 to generate a respective output (e.g., a respective noisyreconstructed sample 113 or a respective clean reconstructed sample 114)based on the unlabeled sample.

In some non-limiting embodiments or aspects, input layer 132 ofautoencoder network 120 may receive each respective unlabeled sample asinput. Each respective input may be forward-propagated from input layer132 through hidden layer(s) 134 to transform the respective input into alatent representation (e.g., a vector in a latent space, which may be acompressed representation of the respective input) at the latent layer140. Additionally or alternatively, the latent representation may beforward-propagated from latent layer 140 through hidden layer(s) 152 totransform the respective latent representation into an output (e.g., areconstruction of the respective input based on the latentrepresentation) at the output layer 154.

As shown in FIG. 2 , at step 210, process 200 may include determining ametric and/or determining whether to label each input sample as noisy orclean. For example, data cleaning system 102 may determine a metricbased on (e.g., difference between) each respective unlabeled sample andthe respective output (e.g., noisy reconstructed sample 113 and/or cleanreconstructed sample 114) generated based thereon. Additionally oralternatively, data cleaning system 102 may determine whether to labeleach unlabeled sample as noisy or clean based on the metric and/or basedon a difference between each respective unlabeled sample and therespective output generated based thereon. For example, data cleaningsystem 102 may determine whether to label each unlabeled sample as noisyor clean based on the metric and a threshold.

In some non-limiting embodiments or aspects, the metric may include adifference between each respective unlabeled sample and the respectiveoutput (e.g., noisy reconstructed sample 113 and/or clean reconstructedsample 114) generated based thereon. For example, data cleaning system102 may determine the difference between each respective unlabeledsample and the respective output generated based thereon. Additionallyor alternatively, data cleaning system 102 may determine whether tolabel the respective unlabeled sample as noisy or clean based on themetric (e.g., difference) satisfying a threshold. For example, datacleaning system 102 may determine to label the respective unlabeledsample as noisy if the difference satisfies (e.g., exceeds) a threshold,or data cleaning system 102 may determine to label the respectiveunlabeled sample as clean if the difference does not satisfy (e.g., doesnot exceed) the threshold.

As shown in FIG. 2 , at step 212, process 200 may include cleaning the(unlabeled) data and/or generating a report based on the (unlabeled)data. For example, data cleaning system 102 may clean the unlabeleddata. Additionally or alternatively, data cleaning system 102 maygenerate a report based on the unlabeled data.

In some non-limiting embodiments or aspects, data cleaning system 102may clean the unlabeled samples by discarding noisy samples 111 (e.g.,based on such samples being labeled as noisy, as described herein). Assuch, only clean samples 112 may remain.

In some non-limiting embodiments or aspects, data cleaning system 102may set a flag indicating each noisy sample 111 has been determined tobe labeled as noisy. As such, all noisy samples 111 in the unlabeleddata may be identified as noisy.

In some non-limiting embodiments or aspects, data cleaning system 102may label each noisy sample 111 as noisy.

In some non-limiting embodiments or aspects, data cleaning system 102may communicate a score based on the metric (e.g., the difference) foreach noisy sample 111 and/or for each sample in the unlabeled data(e.g., all noisy samples 111 and clean samples 112). In somenon-limiting embodiments or aspects, the respective score for arespective sample may be the metric. In some non-limiting embodiments oraspects, the respective score for a respective sample may be anormalized value based on the metric (e.g., a value between 0 and 1,wherein the score is normalized such that the highest metric for any ofthe samples is equal to 1).

In some non-limiting embodiments or aspects, data cleaning system 102may communicate report data based on determining whether to label eachrespective unlabeled sample as noisy or clean. For example, datacleaning system 102 may generate a report with an indication for eachsample of the unlabeled data as noisy or clean. Additionally oralternatively, the report may include the metric (e.g., difference)and/or the score associated with each respective sample of the unlabeleddata. Additionally or alternatively, the report may include an overallscore (e.g., an average of the metric, an average of the score, a medianmetric, a median score, a maximum metric, a maximum score, and/or thelike) for the unlabeled data.

In some non-limiting embodiments or aspects, data cleaning system 102may communicate (e.g., to output data database 108 and/or user device104) the outputs (noisy reconstructed samples 113 and/or cleanreconstructed samples 114), cleaned data (e.g., clean samples 112 afterdiscarding noisy sample 111), a report based on determining whether eachinput sample is noisy or clean, and/or any combination thereof.

In some non-limiting embodiments or aspects, the cleaned data may beused (e.g., by user device 104, another system, another device, anothergroup of systems, or another group of devices, separate from orincluding data cleaning system 102) to train a separate machine learningmodel and/or to perform a task (e.g., classification, prediction,scoring, etc.) based on the cleaned data.

In some non-limiting embodiments or aspects, data cleaning system 102may retrain autoencoder network 120 based on the training data and thecleaned data. For example, if data cleaning system 102 labels eachsample of the unlabeled data as either clean or noisy, the sampleslabeled as noisy may be added to the training data as additional noisysamples 111 and the samples labeled as clean may be added to thetraining data as additional clean samples 112. Additionally oralternatively, noisy reconstructed samples 113 and/or cleanreconstructed samples 114 generated based on the unlabeled data may beadded to the training data as additional noisy samples 111 and/oradditional clean samples 112, respectively.

In some non-limiting embodiments or aspects, the plurality of noisysamples may include a plurality of declined transactions. Additionallyor alternatively, the plurality of other samples may include a firstplurality of approved transactions (which may include true (e.g.,rightfully approved) approved transactions and/or false (e.g.,wrongfully) approved transactions), and/or the plurality of unlabeledsamples may include a second plurality of approved transactions (whichmay include true approved transactions and/or false approvedtransactions). Data cleaning system 102 may receive the plurality ofdeclined transactions, the first plurality of approved transactions,and/or the second plurality of approved transactions, as describedherein. Data cleaning system 102 may train autoencoder network 120 basedon the plurality of declined transactions (e.g., noisy samples) and thefirst plurality of approved transactions (e.g., other/clean samples), asdescribed herein. Additionally or alternatively, data cleaning system102 may input each transaction of the second plurality of approvedtransactions into (trained) autoencoder network 120 to generate outputsbased thereon, and/or data cleaning system 102 may determine a metricbased on (e.g., difference between) each transaction (of the secondplurality of approved transactions) and the output generated basedthereon, as described herein. Data cleaning system 102 may determinewhether to label each respective unlabeled sample as noisy (e.g., falseapproved, i.e., should have been declined) or clean (e.g., trueapproved, i.e., should have been approved) based on the respectivemetric and a threshold, as described herein. In some non-limitingembodiments or aspects, data cleaning system 102 may clean eachrespective transaction of the second plurality of unlabeled samples thatdata cleaning system 102 determined to be labeled as noisy by discardingthe respective transaction, as described herein. Accordingly, aremaining plurality of transactions may include each respectivetransaction (of the second plurality of approved transactions) that datacleaning system 102 determined to be labeled as clean, as describedherein.

In some non-limiting embodiments or aspects, data cleaning system 102may retrain the autoencoder network to increase a further first metricbased on the plurality of declined transactions and a further pluralityof first outputs generated by the autoencoder network using theplurality of declined transactions and to reduce a further second metricbased on the remaining plurality of unlabeled samples and a furtherplurality of second outputs generated by the autoencoder network usingthe remaining plurality of unlabeled samples.

FIG. 3A depicts an implementation 300 a for cleaning noisy data fromunlabeled datasets using autoencoders according to some non-limitingembodiments or aspects. As shown in FIG. 3A, implementation 300 aincludes noisy sample 311 a (e.g., one of a plurality of noisy samples),first unlabeled sample 312 a (e.g., one of a first plurality ofunlabeled samples), second unlabeled sample 312 b (e.g., one of a secondplurality of unlabeled samples), first noisy reconstructed sample 313 a(e.g., one of a first plurality of noisy reconstructed samples), secondnoisy reconstructed sample 313 b (e.g., one of a second plurality ofnoisy reconstructed samples), clean reconstructed sample 314 a (e.g.,one of a plurality of noisy clean samples), and autoencoder network 320(including encoder network 330, input layer 332, hidden layer(s) 334,latent layer 340, decoder network 350, hidden layer(s) 352, and outputlayer 354). In some non-limiting embodiments or aspects, noisy sample311 a may be the same as or similar to noisy samples 111. In somenon-limiting embodiments or aspects, first unlabeled sample 312 a and/orsecond unlabeled sample 312 b may be the same as or similar to unlabeledsamples 112. In some non-limiting embodiments or aspects, first noisyreconstructed sample 313 a and/or second noisy reconstructed sample 313b may be the same as or similar to noisy reconstructed samples 113. Insome non-limiting embodiments or aspects, clean reconstructed sample 314a may be the same as or similar to clean reconstructed samples 114. Insome non-limiting embodiments or aspects, autoencoder network 320,encoder network 330, input layer 332, hidden layer(s) 334, latent layer340, decoder network 350, hidden layer(s) 352, and/or output layer 354may be the same as or similar to autoencoder network 120, encodernetwork 130, input layer 132, hidden layer(s) 134, latent layer 140,decoder network 150, hidden layer(s) 152, and/or output layer 154. Insome non-limiting embodiments or aspects, implementation 300 a may beimplemented (e.g., completely, partially, and/or the like) by datacleaning system 102. In some non-limiting embodiments or aspects,implementation 300 a may be implemented (e.g., completely, partially,and/or the like) by another system, another device, another group ofsystems, or another group of devices, separate from or including datacleaning system 102, such as user device 104, input data database 106,and output data database 108.

In some non-limiting embodiments or aspects, noisy sample 311 a mayinclude a handwriting sample of a number 1. In some non-limitingembodiments or aspects, second unlabeled sample 312 b may include asecond handwriting sample of a number 1. In some non-limitingembodiments or aspects, first unlabeled sample 312 a may include ahandwriting sample of a number 5. For the purpose of illustration,assume handwriting samples of number 1 are relatively noisy (e.g.,non-uniform), and handwriting samples of number 5 are relatively clean.As such, autoencoder network 320 may be trained to maximize a differencebetween noisy sample 311 a and first noisy reconstructed sample 313 aand/or a difference between second unlabeled sample 312 b and secondnoisy reconstructed sample 313 b. Additionally, autoencoder network 320may be trained to minimize a difference between first unlabeled sample312 a and clean reconstructed sample 314 a.

In some non-limiting embodiments or aspects, autoencoder network 320 maybe trained based on noisy samples (e.g., including noisy sample 311 a)and unlabeled samples (e.g., including first unlabeled sample 312 aand/or second unlabeled sample 312 b), as described herein.

In some non-limiting embodiments or aspects, after autoencoder network320 is trained, autoencoder network 320 may generate outputs (e.g.,first noisy reconstructed sample 313 a, second noisy reconstructedsample 313 b, and/or clean reconstructed sample 314 a) based on inputs(e.g., noisy sample 311 a, second unlabeled sample 312 b, and/or firstunlabeled sample 312 a, respectively), as described herein. For example,as shown in FIG. 3A, autoencoder network 320 may increase (e.g.,maximize) the difference between noisy sample 311 a and first noisyreconstructed sample 313 a and/or the difference between secondunlabeled sample 312 b and second noisy reconstructed sample 313 b.Additionally, autoencoder network 320 may reduce (e.g., minimize) thedifference between first unlabeled sample 312 a and clean reconstructedsample 314 a.

FIG. 3B depicts a graph 300 b for an exemplary metric for implementation300 a according to some non-limiting embodiments or aspects. As shown inFIG. 3B, graph 300 b includes labels for handwriting samples as 1 or 5on the horizontal axis and the metric on the vertical axis. In somenon-limiting embodiments or aspects, the metric may include a normalizedscore based on the difference between input handwriting samples andoutputs on the vertical axis, as described herein.

In some non-limiting embodiments or aspects, the distribution of scoresfor handwriting samples labeled as number 1 may be relatively spreadout, with most scores above the threshold. Accordingly, handwritingsamples of the number 1 are mostly noisy.

In some non-limiting embodiments or aspects, the distribution of scoresfor handwriting samples labeled as number 5 may be relatively condensed,with most scores below the threshold. Accordingly, handwriting samplesof the number 5 are mostly clean.

Referring now to FIG. 4 , FIG. 4 is a diagram of non-limitingembodiments or aspects of an environment 400 in which systems, products,and/or methods, as described herein, may be implemented. As shown inFIG. 4 , environment 400 includes transaction service provider system402, issuer system 404, customer device 406, merchant system 408,acquirer system 410, and communication network 412. In some non-limitingembodiments or aspects, each of data cleaning system 102, user device104, input data database 106, and/or output data database 108 may beimplemented by (e.g., part of) transaction service provider system 402.In some non-limiting embodiments or aspects, at least one of user device104, input data database 106, and/or output data database 108 may beimplemented by (e.g., part of) another system, another device, anothergroup of systems, or another group of devices, separate from orincluding transaction service provider system 402, such as issuer system404, merchant system 408, acquirer system 410, and/or the like. Forexample, user device 104 may be the same as or similar to customerdevice 406, and/or user device may be implemented by (e.g., part of)issuer system 404, merchant system 408, or acquirer system 410.

Transaction service provider system 402 may include one or more devicescapable of receiving information from and/or communicating informationto issuer system 404, customer device 406, merchant system 408, and/oracquirer system 410 via communication network 412. For example,transaction service provider system 402 may include a computing device,such as a server (e.g., a transaction processing server), a group ofservers, and/or other like devices. In some non-limiting embodiments oraspects, transaction service provider system 402 may be associated witha transaction service provider as described herein. In some non-limitingembodiments or aspects, transaction service provider system 402 may bein communication with a data storage device, which may be local orremote to transaction service provider system 402. In some non-limitingembodiments or aspects, transaction service provider system 402 may becapable of receiving information from, storing information in,communicating information to, or searching information stored in thedata storage device.

Issuer system 404 may include one or more devices capable of receivinginformation and/or communicating information to transaction serviceprovider system 402, customer device 406, merchant system 408, and/oracquirer system 410 via communication network 412. For example, issuersystem 404 may include a computing device, such as a server, a group ofservers, and/or other like devices. In some non-limiting embodiments oraspects, issuer system 404 may be associated with an issuer institutionas described herein. For example, issuer system 404 may be associatedwith an issuer institution that issued a credit account, debit account,credit card, debit card, and/or the like to a user associated withcustomer device 406.

Customer device 406 may include one or more devices capable of receivinginformation from and/or communicating information to transaction serviceprovider system 402, issuer system 404, merchant system 408, and/oracquirer system 410 via communication network 412. Additionally oralternatively, each customer device 406 may include a device capable ofreceiving information from and/or communicating information to othercustomer devices 406 via communication network 412, another network(e.g., an ad hoc network, a local network, a private network, a virtualprivate network, and/or the like), and/or any other suitablecommunication technique. For example, customer device 406 may include aclient device and/or the like. In some non-limiting embodiments oraspects, customer device 406 may or may not be capable of receivinginformation (e.g., from merchant system 408 or from another customerdevice 406) via a short-range wireless communication connection (e.g.,an NFC communication connection, an RFID communication connection, aBluetooth® communication connection, a Zigbee® communication connection,and/or the like), and/or communicating information (e.g., to merchantsystem 408) via a short-range wireless communication connection.

Merchant system 408 may include one or more devices capable of receivinginformation from and/or communicating information to transaction serviceprovider system 402, issuer system 404, customer device 406, and/oracquirer system 410 via communication network 412. Merchant system 408may also include a device capable of receiving information from customerdevice 406 via communication network 412, a communication connection(e.g., an NFC communication connection, an RFID communicationconnection, a Bluetooth® communication connection, a Zigbee®communication connection, and/or the like) with customer device 406,and/or the like, and/or communicating information to customer device 406via communication network 412, the communication connection, and/or thelike. In some non-limiting embodiments or aspects, merchant system 408may include a computing device, such as a server, a group of servers, aclient device, a group of client devices, and/or other like devices. Insome non-limiting embodiments or aspects, merchant system 408 may beassociated with a merchant as described herein. In some non-limitingembodiments or aspects, merchant system 408 may include one or moreclient devices. For example, merchant system 408 may include a clientdevice that allows a merchant to communicate information to transactionservice provider system 402. In some non-limiting embodiments oraspects, merchant system 408 may include one or more devices, such ascomputers, computer systems, and/or peripheral devices capable of beingused by a merchant to conduct a transaction with a user. For example,merchant system 408 may include a POS device and/or a POS system.

Acquirer system 410 may include one or more devices capable of receivinginformation from and/or communicating information to transaction serviceprovider system 402, issuer system 404, customer device 406, and/ormerchant system 408 via communication network 412. For example, acquirersystem 410 may include a computing device, a server, a group of servers,and/or the like. In some non-limiting embodiments or aspects, acquirersystem 410 may be associated with an acquirer as described herein.

Communication network 412 may include one or more wired and/or wirelessnetworks. For example, communication network 412 may include a cellularnetwork (e.g., a long-term evolution (LTE) network, a third generation(4G) network, a fourth generation (4G) network, a fifth generation (5G)network, a code division multiple access (CDMA) network, and/or thelike), a public land mobile network (PLMN), a local area network (LAN),a wide area network (WAN), a metropolitan area network (MAN), atelephone network (e.g., the public switched telephone network (PSTN)),a private network (e.g., a private network associated with a transactionservice provider), an ad hoc network, an intranet, the Internet, a fiberoptic-based network, a cloud computing network, and/or the like, and/ora combination of these or other types of networks.

In some non-limiting embodiments or aspects, processing a transactionmay include generating and/or communicating at least one transactionmessage (e.g., authorization request, authorization response, anycombination thereof, and/or the like). For example, a client device(e.g., customer device 406, a POS device of merchant system 408, and/orthe like) may initiate the transaction, e.g., by generating anauthorization request. Additionally or alternatively, the client device(e.g., customer device 406, at least one device of merchant system 408,and/or the like) may communicate the authorization request. For example,customer device 406 may communicate the authorization request tomerchant system 408 and/or a payment gateway (e.g., a payment gateway oftransaction service provider system 402, a third-party payment gatewayseparate from transaction service provider system 402, and/or the like).Additionally or alternatively, merchant system 408 (e.g., a POS devicethereof) may communicate the authorization request to acquirer system410 and/or a payment gateway. In some non-limiting embodiments oraspects, acquirer system 410 and/or a payment gateway may communicatethe authorization request to transaction service provider system 402and/or issuer system 404. Additionally or alternatively, transactionservice provider system 402 may communicate the authorization request toissuer system 404. In some non-limiting embodiments or aspects, issuersystem 404 may determine an authorization decision (e.g., authorize,decline, and/or the like) based on the authorization request. Forexample, the authorization request may cause issuer system 404 todetermine the authorization decision based thereof. In some non-limitingembodiments or aspects, issuer system 404 may generate an authorizationresponse based on the authorization decision. Additionally oralternatively, issuer system 404 may communicate the authorizationresponse. For example, issuer system 404 may communicate theauthorization response to transaction service provider system 402 and/ora payment gateway. Additionally or alternatively, transaction serviceprovider system 402 and/or a payment gateway may communicate theauthorization response to acquirer system 410, merchant system 408,and/or customer device 406. Additionally or alternatively, acquirersystem 410 may communicate the authorization response to merchant system408 and/or a payment gateway. Additionally or alternatively, a paymentgateway may communicate the authorization response to merchant system408 and/or customer device 406. Additionally or alternatively, merchantsystem 408 may communicate the authorization response to customer device406. In some non-limiting embodiments or aspects, merchant system 408may receive (e.g., from acquirer system 410 and/or a payment gateway)the authorization response. Additionally or alternatively, merchantsystem 408 may complete the transaction based on the authorizationresponse (e.g., provide, ship, and/or deliver goods and/or servicesassociated with the transaction; fulfill an order associated with thetransaction; any combination thereof; and/or the like).

For the purpose of illustration, processing a transaction may includegenerating a transaction message (e.g., authorization request and/or thelike) based on an account identifier of a customer (e.g., associatedwith customer device 406 and/or the like) and/or transaction dataassociated with the transaction. For example, merchant system 408 (e.g.,a client device of merchant system 408, a POS device of merchant system408, and/or the like) may initiate the transaction, e.g., by generatingan authorization request (e.g., in response to receiving the accountidentifier from a portable financial device of the customer and/or thelike). Additionally or alternatively, merchant system 408 maycommunicate the authorization request to acquirer system 410.Additionally or alternatively, acquirer system 410 may communicate theauthorization request to transaction service provider system 402.Additionally or alternatively, transaction service provider system 402may communicate the authorization request to issuer system 404. Issuersystem 404 may determine an authorization decision (e.g., authorize,decline, and/or the like) based on the authorization request, and/orissuer system 404 may generate an authorization response based on theauthorization decision and/or the authorization request. Additionally oralternatively, issuer system 404 may communicate the authorizationresponse to transaction service provider system 402. Additionally oralternatively, transaction service provider system 402 may communicatethe authorization response to acquirer system 410, which may communicatethe authorization response to merchant system 408.

For the purpose of illustration, clearing and/or settlement of atransaction may include generating a message (e.g., clearing message,settlement message, and/or the like) based on an account identifier of acustomer (e.g., associated with customer device 406 and/or the like)and/or transaction data associated with the transaction. For example,merchant system 408 may generate at least one clearing message (e.g., aplurality of clearing messages, a batch of clearing messages, and/or thelike). Additionally or alternatively, merchant system 408 maycommunicate the clearing message(s) to acquirer system 410. Additionallyor alternatively, acquirer system 410 may communicate the clearingmessage(s) to transaction service provider system 402. Additionally oralternatively, transaction service provider system 402 may communicatethe clearing message(s) to issuer system 404. Additionally oralternatively, issuer system 404 may generate at least one settlementmessage based on the clearing message(s). Additionally or alternatively,issuer system 404 may communicate the settlement message(s) and/or fundsto transaction service provider system 402 (and/or a settlement banksystem associated with transaction service provider system 402).Additionally or alternatively, transaction service provider system 402(and/or the settlement bank system) may communicate the settlementmessage(s) and/or funds to acquirer system 410, which may communicatethe settlement message(s) and/or funds to merchant system 408 (and/or anaccount associated with merchant system 408).

The number and arrangement of systems, devices, and/or networks shown inFIG. 4 are provided as an example. There may be additional systems,devices, and/or networks; fewer systems, devices, and/or networks;different systems, devices, and/or networks; and/or differently arrangedsystems, devices, and/or networks than those shown in FIG. 4 .Furthermore, two or more systems or devices shown in FIG. 4 may beimplemented within a single system or device, or a single system ordevice shown in FIG. 4 may be implemented as multiple distributedsystems or devices. Additionally or alternatively, a set of systems(e.g., one or more systems) or a set of devices (e.g., one or moredevices) of environment 400 may perform one or more functions describedas being performed by another set of systems or another set of devicesof environment 400.

Referring now to FIG. 5 , shown is a diagram of example components of adevice 900 according to non-limiting embodiments or aspects. Device 900may correspond to data cleaning system 102, user device 104, input datadatabase 106, and/or output data database 108 in FIG. 1 and/ortransaction service provider system 402, issuer system 404, customerdevice 406, merchant system 408, and/or acquirer system 410 in FIG. 4 ,as an example. In some non-limiting embodiments or aspects, such systemsor devices may include at least one device 900 and/or at least onecomponent of device 900. The number and arrangement of components shownare provided as an example. In some non-limiting embodiments or aspects,device 900 may include additional components, fewer components,different components, or differently arranged components than thoseshown in FIG. 5 . Additionally or alternatively, a set of components(e.g., one or more components) of device 900 may perform one or morefunctions described as being performed by another set of components ofdevice 900.

As shown in FIG. 5 , device 900 may include bus 902, processor 904,memory 906, storage component 908, input component 910, output component912, and communication interface 914. Bus 902 may include a componentthat permits communication among the components of device 900. In somenon-limiting embodiments or aspects, processor 904 may be implemented inhardware, software, or a combination of hardware and software. Forexample, processor 904 may include a processor (e.g., a centralprocessing unit (CPU), a graphics processing unit (GPU), an acceleratedprocessing unit (APU), etc.), a microprocessor, a digital signalprocessor (DSP), and/or any processing component (e.g., afield-programmable gate array (FPGA), an application-specific integratedcircuit (ASIC), etc.) that can be programmed to perform a function.Memory 906 may include random access memory (RAM), read only memory(ROM), and/or another type of dynamic or static storage device (e.g.,flash memory, magnetic memory, optical memory, etc.) that storesinformation and/or instructions for use by processor 904.

With continued reference to FIG. 5 , storage component 908 may storeinformation and/or software related to the operation and use of device900. For example, storage component 908 may include a hard disk (e.g., amagnetic disk, an optical disk, a magneto-optic disk, a solid statedisk, etc.) and/or another type of computer-readable medium. Inputcomponent 910 may include a component that permits device 900 to receiveinformation, such as via user input (e.g., a touch screen display, akeyboard, a keypad, a mouse, a button, a switch, a microphone, etc.).Additionally, or alternatively, input component 910 may include a sensorfor sensing information (e.g., a global positioning system (GPS)component, an accelerometer, a gyroscope, an actuator, etc.). Outputcomponent 912 may include a component that provides output informationfrom device 900 (e.g., a display, a speaker, one or more light-emittingdiodes (LEDs), etc.). Communication interface 914 may include atransceiver-like component (e.g., a transceiver, a separate receiver andtransmitter, etc.) that enables device 900 to communicate with otherdevices, such as via a wired connection, a wireless connection, or acombination of wired and wireless connections. Communication interface914 may permit device 900 to receive information from another deviceand/or provide information to another device. For example, communicationinterface 914 may include an Ethernet interface, an optical interface, acoaxial interface, an infrared interface, a radio frequency (RF)interface, a universal serial bus (USB) interface, a Wi-Fi® interface, acellular network interface, and/or the like.

Device 900 may perform one or more processes described herein. Device900 may perform these processes based on processor 904 executingsoftware instructions stored by a computer-readable medium, such asmemory 906 and/or storage component 908. A computer-readable medium mayinclude any non-transitory memory device. A non-transitory memory deviceincludes memory space located inside of a single physical storage deviceor memory space spread across multiple physical storage devices.Software instructions may be read into memory 906 and/or storagecomponent 908 from another computer-readable medium or from anotherdevice via communication interface 914. When executed, softwareinstructions stored in memory 906 and/or storage component 908 may causeprocessor 904 to perform one or more processes described herein.Additionally or alternatively, hardwired circuitry may be used in placeof or in combination with software instructions to perform one or moreprocesses described herein. Thus, embodiments described herein are notlimited to any specific combination of hardware circuitry and software.The term “programmed or configured,” as used herein, refers to anarrangement of software, hardware circuitry, or any combination thereofon one or more devices.

Although embodiments or aspects have been described in detail for thepurpose of illustration, it is to be understood that such detail issolely for that purpose and that the disclosure is not limited to thedisclosed embodiments or aspects, but, on the contrary, is intended tocover modifications and equivalent arrangements that are within thespirit and scope of the appended claims. For example, it is to beunderstood that the present disclosure contemplates that, to the extentpossible, one or more features of any embodiment or aspect can becombined with one or more features of any other embodiment or aspect.

What is claimed is:
 1. A computer-implemented method, comprising: receiving, with at least one processor, training data comprising a plurality of noisy samples labeled as noisy and a plurality of other samples not labeled as noisy; training, with at least one processor, an autoencoder network based on the training data to increase a first metric based on the plurality of noisy samples and a plurality of first outputs generated by the autoencoder network using the plurality of noisy samples and to reduce a second metric based on the plurality of other samples and a plurality of second outputs generated by the autoencoder network using the plurality of other samples; receiving, with at least one processor, unlabeled data comprising a plurality of unlabeled samples; generating, with at least one processor, a plurality of third outputs by the autoencoder network based on the plurality of unlabeled samples; for each respective unlabeled sample of the plurality of unlabeled samples, determining, with at least one processor, a respective third metric based on the respective unlabeled sample and a respective third output of the plurality of third outputs; for each respective unlabeled sample of the plurality of unlabeled samples, determining, with at least one processor, whether to label the respective unlabeled sample as noisy or clean based on the respective third metric and a threshold; and for each respective unlabeled sample determined to be labeled as noisy, cleaning, with at least one processor, the respective unlabeled sample.
 2. The method of claim 1, wherein the plurality of other samples comprises a plurality of clean samples labeled as clean.
 3. The method of claim 1, wherein the plurality of other samples comprises a subset of the plurality of unlabeled samples.
 4. The method of claim 1, wherein the plurality of other samples comprises a second plurality of unlabeled samples, the method further comprising: labeling, with at least one processor, the second plurality of unlabeled samples as clean.
 5. The method of claim 1, wherein the autoencoder network comprises a min-max adversarial hybrid autoencoder.
 6. The method of claim 1, wherein training the autoencoder network comprises training the autoencoder network to maximize a difference between the plurality of noisy samples and the plurality of first outputs and to minimize a difference between the plurality of other samples and the plurality of second outputs.
 7. The method of claim 1, wherein training the autoencoder network comprises: determining a negative mean squared error based on the plurality of noisy samples and the plurality of first outputs as a first component of loss; and determining a mean squared error based on the plurality of other samples and the plurality of second outputs as a second component of loss.
 8. The method of claim 1, wherein the third metric comprises a difference between each respective unlabeled sample and the respective third output, and wherein determining whether to label the respective unlabeled sample as noisy or clean comprises: determining to label the respective unlabeled sample as noisy if the difference exceeds the threshold; or determining to label the respective unlabeled sample as clean if the difference does not exceed the threshold.
 9. The method of claim 1, wherein cleaning the respective unlabeled sample comprises at least one of: discarding the respective unlabeled sample; setting a respective flag indicating that the respective unlabeled sample is determined to be labeled as noisy; labeling the respective unlabeled sample as noisy; communicating a score based on the metric for the respective unlabeled sample; communicating report data based on determining whether to label each respective unlabeled sample as noisy or clean; or any combination thereof.
 10. The method of claim 1, wherein the plurality of noisy samples comprises a plurality of declined transactions, the plurality of other samples comprises a first plurality of approved transactions, and the plurality of unlabeled samples comprises a second plurality of approved transactions, wherein determining whether to label each respective unlabeled sample as noisy or clean comprises determining whether to label each respective unlabeled sample as declined or approved, respectively, wherein cleaning each respective unlabeled sample determined to be labeled as noisy comprises discarding the respective unlabeled sample, and wherein a remaining plurality of unlabeled samples comprises each respective unlabeled sample determined to be labeled as clean.
 11. The method of claim 10, further comprising: retraining, with at least one processor, the autoencoder network to increase a further first metric based on the plurality of declined transactions and a further plurality of first outputs generated by the autoencoder network using the plurality of declined transactions and to reduce a further second metric based on the remaining plurality of unlabeled samples and a further plurality of second outputs generated by the autoencoder network using the remaining plurality of unlabeled samples.
 12. The method of claim 1, wherein receiving the training data comprises receiving the training data from a user device; and wherein receiving the unlabeled data comprises receiving the unlabeled data from the user device.
 13. The method of claim 12, wherein cleaning comprises: generating report data based on determining whether to label each respective unlabeled sample as noisy or clean; and communicating the report data to the user device.
 14. A system, comprising: a data cleaning system configured to: receive training data comprising a plurality of noisy samples labeled as noisy and a plurality of other samples not labeled as noisy; train an autoencoder network based on the training data to increase a first metric based on the plurality of noisy samples and a plurality of first outputs generated by the autoencoder network using the plurality of noisy samples and to reduce a second metric based on the plurality of other samples and a plurality of second outputs generated by the autoencoder network using the plurality of other samples; receive unlabeled data comprising a plurality of unlabeled samples; generate a plurality of third outputs by the autoencoder network based on the plurality of unlabeled samples; for each respective unlabeled sample of the plurality of unlabeled samples, determine a respective third metric based on the respective unlabeled sample and a respective third output of the plurality of third outputs; for each respective unlabeled sample of the plurality of unlabeled samples, determine whether to label the respective unlabeled sample as noisy or clean based on the respective third metric and a threshold; and for each respective unlabeled sample determined to be labeled as noisy, clean the respective unlabeled sample.
 15. The system of claim 14, further comprising: an input data database configured to: receive the training data from a user device; receive the unlabeled data from the user device; and communicate the training data and the unlabeled data to the data cleaning system.
 16. The system of claim 14, wherein cleaning comprises generating report data based on determining whether to label each respective unlabeled sample as noisy or clean and communicating the report data.
 17. The system of claim 16, further comprising: an output data database configured to: receive the report data from the data cleaning system; and communicate the report data to a user device.
 18. The system of claim 14, wherein the data cleaning system comprises part of a transaction service provider system, and wherein a user device comprises part of an issuer system.
 19. A computer program product comprising at least one non-transitory computer-readable medium including one or more instructions that, when executed by at least one processor, cause the at least one processor to: receive training data comprising a plurality of noisy samples labeled as noisy and a plurality of other samples not labeled as noisy; train an autoencoder network based on the training data to increase a first metric based on the plurality of noisy samples and a plurality of first outputs generated by the autoencoder network using the plurality of noisy samples and to reduce a second metric based on the plurality of other samples and a plurality of second outputs generated by the autoencoder network using the plurality of other samples; receive unlabeled data comprising a plurality of unlabeled samples; generate a plurality of third outputs by the autoencoder network based on the plurality of unlabeled samples; for each respective unlabeled sample of the plurality of unlabeled samples, determine a respective third metric based on the respective unlabeled sample and a respective third output of the plurality of third outputs; for each respective unlabeled sample of the plurality of unlabeled samples, determine whether to label the respective unlabeled sample as noisy or clean based on the respective third metric and a threshold; and for each respective unlabeled sample determined to be labeled as noisy, clean the respective unlabeled sample.
 20. The computer program product of claim 19, wherein the plurality of noisy samples comprises a plurality of declined transactions, the plurality of other samples comprises a first plurality of approved transactions, and the plurality of unlabeled samples comprises a second plurality of approved transactions. 